As is known in the art, so-called large-area microscopy, sampling, super-resolution (SR) and image mosaicing techniques find use in many applications. For example, demand for low-cost electronic devices, along with advances in materials, drives semiconductor and device manufacturing toward micro-scale and nano-scale patterns in relatively large areas. Similarly, imaging devices such as microscopes might be desirable for scientific and medical imaging. To inspect such micro and non-scale patterns (so-called high resolution patterns) over a large area requires high-precision imaging technologies. For example, fast frame grabbers and optical microscopy techniques facilitate imaging at micrometer and nanometer scales. However, the field of view (FOV) of such high-resolution microscopes fundamentally limits detailed pattern imaging over a large area.
Some current large-area microscopy solutions employ large FOV and high-resolution optical sensors, such as higher-powered optics and coupled device (CCD) arrays. However, such sensors increase the cost of an imaging system. Other current large-area microscopy solutions implement lens-free large-area imaging systems having relatively large FOV using computational on-chip imaging tools or miniaturized mirror optics. Some on-chip imaging techniques employ digital optoelectronic sensor arrays to directly sample light transmitted through a large-area specimen without using lenses between the specimen and sensor chip. Miniaturized mirror optics systems employ various mirror shapes and projective geometries to reflect light from mirror arrays into a FOV of a camera where the mirror array FOP is relatively large compared with the FOV of the camera. However, both on-chip imaging and miniaturized mirror optics systems have limited spatial resolution. Moreover, on-chip imaging is limited to transmission microscopy modalities, and miniaturized mirror optics experience distortion and low contrast (e.g., due to variations or defects in mirror surfaces, etc.).
An alternative approach to large-area microscopy is to implement high-precision scanners at an effective scanning rate and stitch individual FOV images together into a wide view. During this process, scanners acquire multiple frames over a region of interest (ROI). Raster scanning is commonly employed for scanning small-scale features over large areas. In raster scanning, samples are scanned back and forth in one Cartesian coordinate, and shifted in discrete steps in another Cartesian coordinate.
Multiple-image super-resolution (SR) techniques use sub-pixel overlapping of low-resolution (LR) images to reconstruct a high-resolution (HR) image. Typically, motion estimation of LR images is important for SR techniques, as poor motion estimation and subsequent registration are detrimental to SR. For example, signal-to-noise ratios (SNR) below a certain range may cause undesirable registration errors leading to edge jaggedness in the SR image thereby hampering the reconstruction of fine details. Usually image registration can be done either in a frequency or a spatial domain. Most frequency domain methods are based upon the fact that two shifted images differ by only a phase variation that can be estimated using a phase correlation method. Frequency domain methods have robustness to noises and separability of rotational and translational components because of their intrinsic Fourier representation of signals. Spatial domain methods, however, may use either an entire image or extracted feature vectors to estimate relative motion. Image registration techniques may achieve acceptable registration results, however, the widespread uses of these techniques are limited because of the complexity, computational cost and difficulty in validating the results, as well as their sensitivity to imaging conditions.
Instead of using complex image and computationally expensive registration techniques, several attempts have been made to use controlled or known motion between LR images for SR. Such attempts are based on the fact that interpolation for SR can be performed using a set of spatially, regularly shifted LR images and the SR problem can be modeled using the generalized sampling theorem (GST). Using GST, regular shifts of LR images are formulated in a forward image formation matrix and the aliasing is formulated as the combination of frequency sub-bands having different weights in each LR image. The relatively large determinant of the resultant matrix reduces noise amplification. Regular sub-pixel shifts of the LR images can be used to solve the maximization of the determinant for weakly regularized reconstructions.
Most shift-based SR methods concentrate on sampling by regular motion of entire LR images in lateral directions wherein a set of spatially sub-pixel shifted LR images having a first grid spacing are merged into a finer grid by up- and down-sampling techniques. A deblurring filter then may be used to deconvolve the combined image to achieve SR image.
Fast and accurate scanning requires precise positioning with low vibration and short settling times. However, fast positioning relies on high velocities and high accelerations that often induce mechanical vibrations. Techniques for reducing vibration in a raster scan tend to increase the size and cost of mechanical structures (e.g., requiring larger and more robust mechanical supports, etc.), or can be complex and/or sensitive to measurement noise during a scan (e.g., complex control systems, etc.).